@article{VaidSomaniRussaketal.2020,
  author    = {Vaid, Akhil and Somani, Sulaiman and Russak, Adam J. and De Freitas, Jessica K. and Chaudhry, Fayzan F. and Paranjpe, Ishan and Johnson, Kipp W. and Lee, Samuel J. and Miotto, Riccardo and Richter, Felix and Zhao, Shan and Beckmann, Noam D. and Naik, Nidhi and Kia, Arash and Timsina, Prem and Lala, Anuradha and Paranjpe, Manish and Golden, Eddye and Danieletto, Matteo and Singh, Manbir and Meyer, Dara and O'Reilly, Paul F. and Huckins, Laura and Kovatch, Patricia and Finkelstein, Joseph and Freeman, Robert M. and Argulian, Edgar and Kasarskis, Andrew and Percha, Bethany and Aberg, Judith A. and Bagiella, Emilia and Horowitz, Carol R. and Murphy, Barbara and Nestler, Eric J. and Schadt, Eric E. and Cho, Judy H. and Cordon-Cardo, Carlos and Fuster, Valentin and Charney, Dennis S. and Reich, David L. and B{\"o}ttinger, Erwin and Levin, Matthew A. and Narula, Jagat and Fayad, Zahi A. and Just, Allan C. and Charney, Alexander W. and Nadkarni, Girish N. and Glicksberg, Benjamin S.},
  title     = {Machine learning to predict mortality and critical events in a cohort of patients with COVID-19 in New York City: model development and validation},
  series = {Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR},
  volume    = {22},
  journal   = {Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR},
  number    = {11},
  publisher = {Healthcare World},
  address   = {Richmond, Va.},
  issn      = {1439-4456},
  doi       = {10.2196/24018},
  pages     = {19},
  year      = {2020},
  abstract  = {Background: COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. Objective: The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. Methods: We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. Results: Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. Conclusions: We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.},
  language  = {en}
}
@phdthesis{Wozny2019,
  author    = {Wozny, Florian},
  title     = {Three empirical essays in health economics},
  doi       = {10.25932/publishup-46991},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-469910},
  school      = {Universit{\"a}t Potsdam},
  pages     = {200},
  year      = {2019},
  abstract  = {Modern health care systems are characterized by pronounced prevention and cost-optimized treatments. This dissertation offers novel empirical evidence on how useful such measures can be. The first chapter analyzes how radiation, a main pollutant in health care, can negatively affect cognitive health. The second chapter focuses on the effect of Low Emission Zones on public heath, as air quality is the major external source of health problems. Both chapters point out potentials for preventive measures. Finally, chapter three studies how changes in treatment prices affect the reallocation of hospital resources. In the following, I briefly summarize each chapter and discuss implications for health care systems as well as other policy areas. Based on the National Educational Panel Study that is linked to data on radiation, chapter one shows that radiation can have negative long-term effects on cognitive skills, even at subclinical doses. Exploiting arguably exogenous variation in soil contamination in Germany due to the Chernobyl disaster in 1986, the findings show that people exposed to higher radiation perform significantly worse in cognitive tests 25 years later. Identification is ensured by abnormal rainfall within a critical period of ten days. The results show that the effect is stronger among older cohorts than younger cohorts, which is consistent with radiation accelerating cognitive decline as people get older. On average, a one-standarddeviation increase in the initial level of CS137 (around 30 chest x-rays) is associated with a decrease in the cognitive skills by 4.1 percent of a standard deviation (around 0.05 school years). Chapter one shows that sub-clinical levels of radiation can have negative consequences even after early childhood. This is of particular importance because most of the literature focuses on exposure very early in life, often during pregnancy. However, population exposed after birth is over 100 times larger. These results point to substantial external human capital costs of radiation which can be reduced by choices of medical procedures. There is a large potential for reductions because about one-third of all CT scans are assumed to be not medically justified (Brenner and Hall, 2007). If people receive unnecessary CT scans because of economic incentives, this chapter points to additional external costs of health care policies. Furthermore, the results can inform the cost-benefit trade-off for medically indicated procedures. Chapter two provides evidence about the effectiveness of Low Emission Zones. Low Emission Zones are typically justified by improvements in population health. However, there is little evidence about the potential health benefits from policy interventions aiming at improving air quality in inner-cities. The chapter ask how the coverage of Low Emission Zones air pollution and hospitalization, by exploiting variation in the roll out of Low Emission Zones in Germany. It combines information on the geographic coverage of Low Emission Zones with rich panel data on the universe of German hospitals over the period from 2006 to 2016 with precise information on hospital locations and the annual frequency of detailed diagnoses. In order to establish that our estimates of Low Emission Zones' health impacts can indeed be attributed to improvements in local air quality, we use data from Germany's official air pollution monitoring system and assign monitor locations to Low Emission Zones and test whether measures of air pollution are affected by the coverage of a Low Emission Zone. Results in chapter two confirm former results showing that the introduction of Low Emission Zones improved air quality significantly by reducing NO2 and PM10 concentrations. Furthermore, the chapter shows that hospitals which catchment areas are covered by a Low Emission Zone, diagnose significantly less air pollution related diseases, in particular by reducing the incidents of chronic diseases of the circulatory and the respiratory system. The effect is stronger before 2012, which is consistent with a general improvement in the vehicle fleet's emission standards. Depending on the disease, a one-standard-deviation increase in the coverage of a hospitals catchment area covered by a Low Emission Zone reduces the yearly number of diagnoses up to 5 percent. These findings have strong implications for policy makers. In 2015, overall costs for health care in Germany were around 340 billion euros, of which 46 billion euros for diseases of the circulatory system, making it the most expensive type of disease caused by 2.9 million cases (Statistisches Bundesamt, 2017b). Hence, reductions in the incidence of diseases of the circulatory system may directly reduce society's health care costs. Whereas chapter one and two study the demand-side in health care markets and thus preventive potential, chapter three analyzes the supply-side. By exploiting the same hospital panel data set as in chapter two, chapter three studies the effect of treatment price shocks on the reallocation of hospital resources in Germany. Starting in 2005, the implementation of the German-DRG-System led to general idiosyncratic treatment price shocks for individual hospitals. Thus far there is little evidence of the impact of general price shocks on the reallocation of hospital resources. Additionally, I add to the exiting literature by showing that price shocks can have persistent effects on hospital resources even when these shocks vanish. However, simple OLS regressions would underestimate the true effect, due to endogenous treatment price shocks. I implement a novel instrument variable strategy that exploits the exogenous variation in the number of days of snow in hospital catchment areas. A peculiarity of the reform allowed variation in days of snow to have a persistent impact on treatment prices. I find that treatment price increases lead to increases in input factors such as nursing staff, physicians and the range of treatments offered but to decreases in the treatment volume. This indicates supplier-induced demand. Furthermore, the probability of hospital mergers and privatization decreases. Structural differences in pre-treatment characteristics between hospitals enhance these effects. For instance, private and larger hospitals are more affected. IV estimates reveal that OLS results are biased towards zero in almost all dimensions because structural hospital differences are correlated with the reallocation of hospital resources. These results are important for several reasons. The G-DRG-Reform led to a persistent polarization of hospital resources, as some hospitals were exposed to treatment price increases, while others experienced reductions. If hospitals increase the treatment volume as a response to price reductions by offering unnecessary therapies, it has a negative impact on population wellbeing and public spending. However, results show a decrease in the range of treatments if prices decrease. Hospitals might specialize more, thus attracting more patients. From a policy perspective it is important to evaluate if such changes in the range of treatments jeopardize an adequate nationwide provision of treatments. Furthermore, the results show a decrease in the number of nurses and physicians if prices decrease. This could partly explain the nursing crisis in German hospitals. However, since hospitals specialize more they might be able to realize efficiency gains which justify reductions in input factors without loses in quality. Further research is necessary to provide evidence for the impact of the G-DRG-Reform on health care quality. Another important aspect are changes in the organizational structure. Many public hospitals have been privatized or merged. The findings show that this is at least partly driven by the G-DRG-Reform. This can again lead to a lack in services offered in some regions if merged hospitals specialize more or if hospitals are taken over by ecclesiastical organizations which do not provide all treatments due to moral conviction. Overall, this dissertation reveals large potential for preventive health care measures and helps to explain reallocation processes in the hospital sector if treatment prices change. Furthermore, its findings have potentially relevant implications for other areas of public policy. Chapter one identifies an effect of low dose radiation on cognitive health. As mankind is searching for new energy sources, nuclear power is becoming popular again. However, results of chapter one point to substantial costs of nuclear energy which have not been accounted yet. Chapter two finds strong evidence that air quality improvements by Low Emission Zones translate into health improvements, even at relatively low levels of air pollution. These findings may, for instance, be of relevance to design further policies targeted at air pollution such as diesel bans. As pointed out in chapter three, the implementation of DRG-Systems may have unintended side-effects on the reallocation of hospital resources. This may also apply to other providers in the health care sector such as resident doctors.},
  language  = {en}
}
@misc{Yapar2018,
  type      = {Master Thesis},
  author    = {Yapar, Diren},
  title     = {Linguistic Landscapes - Eine Untersuchung zur Repr{\"a}sentation von visueller Mehrsprachigkeit in Berliner Krankenh{\"a}usern},
  doi       = {10.25932/publishup-46001},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-460019},
  school      = {Universit{\"a}t Potsdam},
  pages     = {II, 55},
  year      = {2018},
  abstract  = {Die empirische Studie untersucht, inwieweit die gesellschaftliche Mehrsprachigkeit in Berlin in den verschiedenen Beschilderungstypen der Berliner Krankenh{\"a}user vertreten ist. Damit f{\"u}gt sich die Arbeit thematisch in die Untersuchung von "Sprachlandschaften" ein, einem neu entstehenden soziolinguistisch orientierten Forschungsfeld, das Zusammenh{\"a}nge zwischen sozialer Mehrsprachigkeit und ihrer {\"o}ffentlichen visuellen Repr{\"a}sentation untersucht und aufdeckt. Welche Sprachen sind in welchen Diskurstypen in Berliner Krankenh{\"a}usern sichtbar? Wie entwickelt sich die Entscheidungspolitik, auf deren Grundlage Mehrsprachigkeit in Krankenh{\"a}usern sichtbar wird? F{\"u}r die Befragung wurde jedes Krankenhaus in jedem der zw{\"o}lf Berliner Bezirke besucht und die Ergebnisse durch Bilddateien dokumentiert. Das Ergebnis dieser Studie ist ein umfassendes Korpus.},
  language  = {de}
}